Pharmacy

Oritavancin and dalbavancin are long acting lipoglycopeptides indicated for the treatment of acute bacterial skin and skin structure infections (ABSSSI).^1,2 Largely due to their long half-lives, prolonged tissue concentrations at sites of infection, tolerability, and minimal requirement for therapeutic drug monitoring, these agents are attractive options in outpatient settings.^3,4 A 1- or 2-dose treatment of oritavancin and dalbavancin may be sufficient for conditions traditionally treated with outpatient parenteral antimicrobial therapy (OPAT) via peripherally inserted central catheter (PICC).

Limited research supports the use of dalbavancin and oritavancin for bone and joint infections, infective endocarditis, and bloodstream infections (BSIs). However, the US Food and Drug Administration has approved an indication for the treatment of ABSSSI.^3-9 Dosing for these off-label indications varies but typically consists of an initial intravenous (IV) dose (1000 mg, 1200 mg, or 1500 mg), with a subsequent dose 1 to 2 weeks later or administered once weekly.^6-10

Due in part to the recent availability of oritavancin and dalbavancin relative to the publication of practice guidelines, their appropriate place in therapy continues to evolve based on emerging literature.^11,12 One potential barrier of use for these medications is their cost. Based on the number of doses administered, the 2022 estimated total acquisition cost of therapy for oritavancin and dalbavancin was $1014 to $4397 and $3046 to $7150, respectively (eAppendix). Despite the high acquisition costs, these agents do not require the placement of an indwelling central line, can be administered in outpatient settings, and require minimal therapeutic dose monitoring compared to vancomycin.^13-15 This medication use evaluation (MUE) compared the total cost of treatment with oritavancin and dalbavancin vs therapies traditionally used for OPAT or prolonged IV inpatient therapy.

METHODS

This retrospective MUE was conducted at the Boise Veterans Affairs Medical Center (BVAMC), a level 2 facility with an extensive rural catchment area. BVAMC provides many OPAT services, including medications, supplies, and dressing changes after initial clinic or inpatient education. Contracted vendors may also assist with at-home nursing care using supplies provided by the BVAMC. Cases were identified using an internal database of OPAT patients and those who received oritavancin or dalbavancin between September 1, 2017, and November 1, 2022. Patients aged ≥ 18 years who received ≥ 1 dose of oritavancin or dalbavancin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were included. Comparator treatments consisting of ≥ 1 week of vancomycin or daptomycin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were identified through review of OPAT and Infectious Diseases service consults during the same timeframe. Patients were excluded if any antibiotic was prescribed by a non- VA clinician, if medications were not provided by OPAT, or if chart review did not identify an ABSSSI, osteomyelitis/ joint infection, or BSI diagnosis.

Electronic medical record review was conducted using a standardized data collection form (eAppendix). Data collected included demographics, infectious diagnosis, treatment administered, administration procedures and related visits and treatment locations, outcomes including clinical failure, adverse events (AEs), and hospital readmission.

Clinical failure was defined as readmission or death due to worsening infection or readmission secondary to a documented potential AE to the evaluated antibiotics within 90 days after initiation. Clinical failures excluded readmissions not associated with infection including comorbidities or elective procedures. AEs included new onset renal failure (serum creatinine ≥ 0.5 mg/dL), neutropenia (neutrophils ≤ 500), thrombocytopenia (platelets < 100,000), eosinophilia (> 15% eosinophils), or creatine phosphokinase > 10 times the upper limit of normal, and Clostridioides difficile (C. difficile) infection. Line complications included thrombophlebitis, local inflammation, or infection requiring line replacement (eAppendix).

A cost-minimization approach was used to assess the total cost of treatment.¹⁶ Patients who received oritavancin or dalbavancin were matched with patients that received vancomycin and daptomycin for the same indication and about 1 month of initiation through the randomization function in Microsoft Excel. This accounted for changes in personnel, nonformulary drug approvals, cost, and changes in practice during the pandemic. Costs were calculated using a decision tree as a base model (Figure 1). In this model, each treatment dyad was assessed for the presence or absence of clinical failure, adverse event (medication and line complications), and treatment setting endpoints. Cost estimates were tabulated for each patient that received treatment using published VA data, literature, pharmacoeconomist guidance, or best faith effort based on workflow. ^17-20 All cost estimates were based on 2022 figures or adjusted for inflation if obtained prior to 2022. Secondary endpoints of this analysis included estimated total cost of medication acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home-health services.

This evaluation was classified by the BVAMC Medication Use Evaluation research determination subcommittee as a quality improvement project and was considered exempt from VA Human Subjects Research requirements based on the VA Policy Handbook guideline 1058.05.

RESULTS

The study identified 44 patients who received dalbavancin or oritavancin between September 1, 2017, and October 31, 2022. Thirty-nine patients were included in the analysis: 24 received oritavancin and 15 received dalbavancin and were matched by indication to 10 patients who received vancomycin and 8 patients who received daptomycin. Three patients could not be matched by indication of ABSSSI (Figure 2). Most patients were male, aged > 65 years, and were treated for osteomyelitis (Table 1). No patients were treated for infective endocarditis. A myriad of concomitant antibiotics were used to treat patients and culture results indicated that most infections treated with oritavancin and dalbavancin were polymicrobial.

The mean total cost of therapy per patient receiving oritavancin, dalbavancin, vancomycin, and daptomycin was $35,630, $59,612, $73,333, and $73,708, respectively (Figure 3). When stratified by indication, 27 patients (69%) in the oritavancin/dalbavancin group were treated for osteomyelitis/ joint infections (16 oritavancin, 11 dalbavancin), 9 patients (23%) were treated for BSI (6 oritavancin, 3 dalbavancin), and 3 patients (8%) were treated for ABSSSI (2 oritavancin, 1 dalbavancin). The mean cost per patient for osteomyelitis/joint infections with oritavancin, dalbavancin, vancomycin, and daptomycin was $34,678, $54,224, $87,488, and $85,044, respectively. The mean cost per patient for BSI for oritavancin, dalbavancin, vancomycin, and daptomycin was $35,048, $75,349, $40,305, and $68,068, respectively. The mean cost per patient for ABSSSI for oritavancin and dalbavancin was $44,771 and $71,672.51.

Estimated total drug cost represents the cost of drug acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home health services. The mean cost per patient of drug-related therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2203, $5924, $3637, and $7146, respectively (Table 2).

The mean cost per patient for osteomyelitis therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2375, $6775, $4164, $8152, respectively. The mean cost of per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $1737, $3475, $2409, and $1016, respectively. The mean cost per patient for oritavancin and dalbavancin for ABSSSI treatment, was $1553 and $3910, respectively.

Setting-related costs include expenses from inpatient admissions and postdischarge stays at community living centers (CLCs), skilled nursing facilities (SNFs), or rehabilitation facilities (RFs) for the duration of antimicrobial therapy. The mean setting-related therapy cost for osteomyelitis treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $27,852, $17,815, $83,324, and $72,856, respectively. The mean setting-related therapy cost per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $33,310, $60,668, $37,734, and $67,074, respectively. The mean setting-related therapy cost per patient for ABSSSI treatment for oritavancin and dalbavancin was $43,218 and $67,762.00, respectively.

Six of 39 patients (15%) had clinical failure: 2 patients with oritavancin and 4 patients with dalbavancin. Four patients were readmitted for worsening infection and 2 for AEs. One patient (13%) in the daptomycin group had clinical failure due to readmission for worsening infection. There was no clinical failure with vancomycin. The costs associated with clinical failure per patient for oritavancin, dalbavancin, vancomycin, and daptomycin were $2925, $23,972, $0, and $3601, respectively (Table 3).

Thirty-eight patients (97%) who received oritavancin or dalbavancin had difficulty adhering to vancomycin or daptomycin OPAT. Oritavancin or dalbavancin was used in 10 patients (26%) who lacked support at home and 15 patients (38%) who had either a contraindication or previous failure with other antimicrobials, which were the most common explanations.

DISCUSSION

Long-acting lipoglycopeptides represent a potential alternative to home IV therapy that can avoid prolonged IV access with traditional OPAT. This offers significant advantages, allowing patients to be discharged from the hospital early, especially in rural areas with little OPAT infrastructure or those with logistic challenges. In this analysis, treatment with oritavancin for osteomyelitis, BSI, or ABSSSI, yielded an estimated cost savings of about $37,000 per patient, compared to treatment of matched indications with vancomycin and daptomycin. For every patient treated with dalbavancin for osteomyelitis, BSI, or ABSSSI, the cost savings was about $13,000 per patient, compared to treatment of matched indications for daptomycin and vancomycin. The estimated cost savings per patient for oritavancin was similar to previously published projections ($30,500 to $55,831).¹⁵

Cost savings were primarily driven by setting-related costs. The greatest contrast between the oritavancin and dalbavancin group compared to the vancomycin and daptomycin group was the length of stay in a postdischarge CLC, SNF, or RF setting. This analysis estimated that for every patient treated with oritavancin for osteomyelitis, the setting-related cost savings per patient was about $55,000 compared with vancomycin, and about $45,000 per patient compared with daptomycin. Furthermore, the estimated setting-related cost savings for osteomyelitis treatment with dalbavancin was about $65,000 compared with vancomycin and about $55,000 compared with daptomycin.

Clinical failure occurred with greater frequency in the oritavancin and dalbavancin groups (15%), compared with the vancomycin (0%) and daptomycin (13%) groups. Although the clinical failure rates in patients with osteomyelitis treated with oritavancin and dalbavancin compared with daptomycin were like those in previously published research (10%-30%), the rates of clinical failure for vancomycin in this analysis were lower than those in the oritavancin and dalbavancin group.^8,21,22 The discrepancy in clinical failure rates between this analysis and previous research is likely due to selection bias. Based on the percentages of clinical failure found in the analysis, it is not surprising to note that the total clinical failure-related cost per patient was higher for oritavancin and dalbavancin compared to vancomycin, but similar between oritavancin and daptomycin.

This analysis also found that 15% of patients in the oritavancin and dalbavancin group experienced an AE compared to 10% of patients in the vancomycin group and none in the daptomycin group. In the oritavancin and dalbavancin group, the 2 most common AEs were infusion-related reactions and C. difficile colitis. Although infusion related reactions are easier to correspond to oritavancin and dalbavancin, it becomes difficult to definitively attribute the occurrence of C. difficile to these drugs as many patients were receiving concomitant antibiotics. Although not a primary or secondary objective, the rate of IV-line AEs were more prevalent in the vancomycin (10%), and daptomycin (13%) groups, compared to none in the oritavancin and dalbavancin group. This finding was expected; oritavancin and dalbavancin do not require a central IV line for administration.

Pharmacoeconomic literature continues to emerge with long-acting lipoglycopeptides. A 2024 Italian retrospective single-center analysis of 62 patients reported mean cost reductions > €3200 per patient (> $3400) given dalbavancin compared with the standard of care for ABSSSI or more deep-seeded infections such as osteomyelitis.²³ A 2023 Spanish observational multicenter analysis of 124 patients with infective endocarditis demonstrated high efficacy, safety and cost-effectiveness with dalbavancin vs conventional treatments, with a mean savings of > €5548 per patient (> $6200).²⁴ An analysis of the implementation of a dalbavancin order pathway for ABSSSI to avert inpatient admissions at 11 US emergency departments found a mean cost savings of $5133 per patient and $1211 per hospitalization day avoided, compared with inpatient usual care.²⁵

Conversely, a multicenter, retrospective study of 209 patients in a community-based health care system failed to show a financial benefit for dalbavancin use when compared to standard of care for ABSSSI with higher readmission rates.²⁶ Turco et al also reported increased cost results for 64 patients who received dalbavancin vs standard of care for ABSSSI.²⁷ These discordant findings in ABSSSI studies may be impacted by the authors' patient selection choices and cost assumptions, especially with significantly cheaper oral alternatives. More data are needed to best identify the optimal therapeutic use for the long-acting lipoglycopeptides.

Limitations

The most significant limitation in this analysis was selection bias: 38 of 39 patients (97%) who received dalbavancin or oritavancin had a documented reason that described why OPAT therapy with traditional medications would not be optimal, including logistics, AEs, or clinical failures. Most patients treated with vancomycin and daptomycin were admitted into a SNF, RF, or CLC for the remainder of their treatment, allowing for closer monitoring and care compared to patients treated with oritavancin and dalbavancin, but at a greater cost. For patients sent to a community based SNF or RF, laboratory data were not available unless internally drawn or documented in the electronic medical record.

Additionally, not all cost data were available from VA sources; some were applied from literature, pharmacoeconomist, or best faith effort based on workflow. The cost data from third party contractors providing OPAT services to some BVAMC patients during the time frame of this analysis were not available. Due to its small sample size, outliers had the potential to affect averages reported and accuracy of the cost analysis. Emerging evidence suggests that daptomycin doses higher than the manufacturer-recommended regimen may be required for select indications, a factor that could affect cost, AEs, and efficacy outcomes.²⁸ The acquisition cost of oritavancin and dalbavancin may vary by institution (ie, VA contract prices vs non- VA contract prices) and change over time. A current assessment of cost is needed to best visualize institutional benefit.

Finally, while the patient demographic of this MUE was highly representative of the demographic treated at the BVAMC (males aged >65 years), it may not be applicable to external patient populations. This analysis evaluated off-label indications for these medications. Consequently, this analysis would likely not be applicable to non-VA institution, as third-party payers (eg, insurance) are unlikely to cover medications for off-label indications.

CONCLUSIONS

This study found cost savings associated with the use of oritavancin and dalbavancin compared with vancomycin and daptomycin, particularly for the treatment of osteomyelitis. As safety and efficacy data continues to emerge, the use of long-acting lipoglycopeptides appears to be an increasingly attractive alternative option compared to traditional outpatient antimicrobial therapy, depending on the structure of the program. Larger, multicenter cost-effectiveness studies are needed to further establish the impact of these novel agents.

Oritavancin and dalbavancin are long acting lipoglycopeptides indicated for the treatment of acute bacterial skin and skin structure infections (ABSSSI).^1,2 Largely due to their long half-lives, prolonged tissue concentrations at sites of infection, tolerability, and minimal requirement for therapeutic drug monitoring, these agents are attractive options in outpatient settings.^3,4 A 1- or 2-dose treatment of oritavancin and dalbavancin may be sufficient for conditions traditionally treated with outpatient parenteral antimicrobial therapy (OPAT) via peripherally inserted central catheter (PICC).

Limited research supports the use of dalbavancin and oritavancin for bone and joint infections, infective endocarditis, and bloodstream infections (BSIs). However, the US Food and Drug Administration has approved an indication for the treatment of ABSSSI.^3-9 Dosing for these off-label indications varies but typically consists of an initial intravenous (IV) dose (1000 mg, 1200 mg, or 1500 mg), with a subsequent dose 1 to 2 weeks later or administered once weekly.^6-10

Due in part to the recent availability of oritavancin and dalbavancin relative to the publication of practice guidelines, their appropriate place in therapy continues to evolve based on emerging literature.^11,12 One potential barrier of use for these medications is their cost. Based on the number of doses administered, the 2022 estimated total acquisition cost of therapy for oritavancin and dalbavancin was $1014 to $4397 and $3046 to $7150, respectively (eAppendix). Despite the high acquisition costs, these agents do not require the placement of an indwelling central line, can be administered in outpatient settings, and require minimal therapeutic dose monitoring compared to vancomycin.^13-15 This medication use evaluation (MUE) compared the total cost of treatment with oritavancin and dalbavancin vs therapies traditionally used for OPAT or prolonged IV inpatient therapy.

METHODS

This retrospective MUE was conducted at the Boise Veterans Affairs Medical Center (BVAMC), a level 2 facility with an extensive rural catchment area. BVAMC provides many OPAT services, including medications, supplies, and dressing changes after initial clinic or inpatient education. Contracted vendors may also assist with at-home nursing care using supplies provided by the BVAMC. Cases were identified using an internal database of OPAT patients and those who received oritavancin or dalbavancin between September 1, 2017, and November 1, 2022. Patients aged ≥ 18 years who received ≥ 1 dose of oritavancin or dalbavancin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were included. Comparator treatments consisting of ≥ 1 week of vancomycin or daptomycin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were identified through review of OPAT and Infectious Diseases service consults during the same timeframe. Patients were excluded if any antibiotic was prescribed by a non- VA clinician, if medications were not provided by OPAT, or if chart review did not identify an ABSSSI, osteomyelitis/ joint infection, or BSI diagnosis.

Electronic medical record review was conducted using a standardized data collection form (eAppendix). Data collected included demographics, infectious diagnosis, treatment administered, administration procedures and related visits and treatment locations, outcomes including clinical failure, adverse events (AEs), and hospital readmission.

Clinical failure was defined as readmission or death due to worsening infection or readmission secondary to a documented potential AE to the evaluated antibiotics within 90 days after initiation. Clinical failures excluded readmissions not associated with infection including comorbidities or elective procedures. AEs included new onset renal failure (serum creatinine ≥ 0.5 mg/dL), neutropenia (neutrophils ≤ 500), thrombocytopenia (platelets < 100,000), eosinophilia (> 15% eosinophils), or creatine phosphokinase > 10 times the upper limit of normal, and Clostridioides difficile (C. difficile) infection. Line complications included thrombophlebitis, local inflammation, or infection requiring line replacement (eAppendix).

A cost-minimization approach was used to assess the total cost of treatment.¹⁶ Patients who received oritavancin or dalbavancin were matched with patients that received vancomycin and daptomycin for the same indication and about 1 month of initiation through the randomization function in Microsoft Excel. This accounted for changes in personnel, nonformulary drug approvals, cost, and changes in practice during the pandemic. Costs were calculated using a decision tree as a base model (Figure 1). In this model, each treatment dyad was assessed for the presence or absence of clinical failure, adverse event (medication and line complications), and treatment setting endpoints. Cost estimates were tabulated for each patient that received treatment using published VA data, literature, pharmacoeconomist guidance, or best faith effort based on workflow. ^17-20 All cost estimates were based on 2022 figures or adjusted for inflation if obtained prior to 2022. Secondary endpoints of this analysis included estimated total cost of medication acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home-health services.

This evaluation was classified by the BVAMC Medication Use Evaluation research determination subcommittee as a quality improvement project and was considered exempt from VA Human Subjects Research requirements based on the VA Policy Handbook guideline 1058.05.

RESULTS

The study identified 44 patients who received dalbavancin or oritavancin between September 1, 2017, and October 31, 2022. Thirty-nine patients were included in the analysis: 24 received oritavancin and 15 received dalbavancin and were matched by indication to 10 patients who received vancomycin and 8 patients who received daptomycin. Three patients could not be matched by indication of ABSSSI (Figure 2). Most patients were male, aged > 65 years, and were treated for osteomyelitis (Table 1). No patients were treated for infective endocarditis. A myriad of concomitant antibiotics were used to treat patients and culture results indicated that most infections treated with oritavancin and dalbavancin were polymicrobial.

The mean total cost of therapy per patient receiving oritavancin, dalbavancin, vancomycin, and daptomycin was $35,630, $59,612, $73,333, and $73,708, respectively (Figure 3). When stratified by indication, 27 patients (69%) in the oritavancin/dalbavancin group were treated for osteomyelitis/ joint infections (16 oritavancin, 11 dalbavancin), 9 patients (23%) were treated for BSI (6 oritavancin, 3 dalbavancin), and 3 patients (8%) were treated for ABSSSI (2 oritavancin, 1 dalbavancin). The mean cost per patient for osteomyelitis/joint infections with oritavancin, dalbavancin, vancomycin, and daptomycin was $34,678, $54,224, $87,488, and $85,044, respectively. The mean cost per patient for BSI for oritavancin, dalbavancin, vancomycin, and daptomycin was $35,048, $75,349, $40,305, and $68,068, respectively. The mean cost per patient for ABSSSI for oritavancin and dalbavancin was $44,771 and $71,672.51.

Estimated total drug cost represents the cost of drug acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home health services. The mean cost per patient of drug-related therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2203, $5924, $3637, and $7146, respectively (Table 2).

The mean cost per patient for osteomyelitis therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2375, $6775, $4164, $8152, respectively. The mean cost of per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $1737, $3475, $2409, and $1016, respectively. The mean cost per patient for oritavancin and dalbavancin for ABSSSI treatment, was $1553 and $3910, respectively.

Setting-related costs include expenses from inpatient admissions and postdischarge stays at community living centers (CLCs), skilled nursing facilities (SNFs), or rehabilitation facilities (RFs) for the duration of antimicrobial therapy. The mean setting-related therapy cost for osteomyelitis treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $27,852, $17,815, $83,324, and $72,856, respectively. The mean setting-related therapy cost per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $33,310, $60,668, $37,734, and $67,074, respectively. The mean setting-related therapy cost per patient for ABSSSI treatment for oritavancin and dalbavancin was $43,218 and $67,762.00, respectively.

Six of 39 patients (15%) had clinical failure: 2 patients with oritavancin and 4 patients with dalbavancin. Four patients were readmitted for worsening infection and 2 for AEs. One patient (13%) in the daptomycin group had clinical failure due to readmission for worsening infection. There was no clinical failure with vancomycin. The costs associated with clinical failure per patient for oritavancin, dalbavancin, vancomycin, and daptomycin were $2925, $23,972, $0, and $3601, respectively (Table 3).

Thirty-eight patients (97%) who received oritavancin or dalbavancin had difficulty adhering to vancomycin or daptomycin OPAT. Oritavancin or dalbavancin was used in 10 patients (26%) who lacked support at home and 15 patients (38%) who had either a contraindication or previous failure with other antimicrobials, which were the most common explanations.

DISCUSSION

Long-acting lipoglycopeptides represent a potential alternative to home IV therapy that can avoid prolonged IV access with traditional OPAT. This offers significant advantages, allowing patients to be discharged from the hospital early, especially in rural areas with little OPAT infrastructure or those with logistic challenges. In this analysis, treatment with oritavancin for osteomyelitis, BSI, or ABSSSI, yielded an estimated cost savings of about $37,000 per patient, compared to treatment of matched indications with vancomycin and daptomycin. For every patient treated with dalbavancin for osteomyelitis, BSI, or ABSSSI, the cost savings was about $13,000 per patient, compared to treatment of matched indications for daptomycin and vancomycin. The estimated cost savings per patient for oritavancin was similar to previously published projections ($30,500 to $55,831).¹⁵

Cost savings were primarily driven by setting-related costs. The greatest contrast between the oritavancin and dalbavancin group compared to the vancomycin and daptomycin group was the length of stay in a postdischarge CLC, SNF, or RF setting. This analysis estimated that for every patient treated with oritavancin for osteomyelitis, the setting-related cost savings per patient was about $55,000 compared with vancomycin, and about $45,000 per patient compared with daptomycin. Furthermore, the estimated setting-related cost savings for osteomyelitis treatment with dalbavancin was about $65,000 compared with vancomycin and about $55,000 compared with daptomycin.

Clinical failure occurred with greater frequency in the oritavancin and dalbavancin groups (15%), compared with the vancomycin (0%) and daptomycin (13%) groups. Although the clinical failure rates in patients with osteomyelitis treated with oritavancin and dalbavancin compared with daptomycin were like those in previously published research (10%-30%), the rates of clinical failure for vancomycin in this analysis were lower than those in the oritavancin and dalbavancin group.^8,21,22 The discrepancy in clinical failure rates between this analysis and previous research is likely due to selection bias. Based on the percentages of clinical failure found in the analysis, it is not surprising to note that the total clinical failure-related cost per patient was higher for oritavancin and dalbavancin compared to vancomycin, but similar between oritavancin and daptomycin.

This analysis also found that 15% of patients in the oritavancin and dalbavancin group experienced an AE compared to 10% of patients in the vancomycin group and none in the daptomycin group. In the oritavancin and dalbavancin group, the 2 most common AEs were infusion-related reactions and C. difficile colitis. Although infusion related reactions are easier to correspond to oritavancin and dalbavancin, it becomes difficult to definitively attribute the occurrence of C. difficile to these drugs as many patients were receiving concomitant antibiotics. Although not a primary or secondary objective, the rate of IV-line AEs were more prevalent in the vancomycin (10%), and daptomycin (13%) groups, compared to none in the oritavancin and dalbavancin group. This finding was expected; oritavancin and dalbavancin do not require a central IV line for administration.

Pharmacoeconomic literature continues to emerge with long-acting lipoglycopeptides. A 2024 Italian retrospective single-center analysis of 62 patients reported mean cost reductions > €3200 per patient (> $3400) given dalbavancin compared with the standard of care for ABSSSI or more deep-seeded infections such as osteomyelitis.²³ A 2023 Spanish observational multicenter analysis of 124 patients with infective endocarditis demonstrated high efficacy, safety and cost-effectiveness with dalbavancin vs conventional treatments, with a mean savings of > €5548 per patient (> $6200).²⁴ An analysis of the implementation of a dalbavancin order pathway for ABSSSI to avert inpatient admissions at 11 US emergency departments found a mean cost savings of $5133 per patient and $1211 per hospitalization day avoided, compared with inpatient usual care.²⁵

Conversely, a multicenter, retrospective study of 209 patients in a community-based health care system failed to show a financial benefit for dalbavancin use when compared to standard of care for ABSSSI with higher readmission rates.²⁶ Turco et al also reported increased cost results for 64 patients who received dalbavancin vs standard of care for ABSSSI.²⁷ These discordant findings in ABSSSI studies may be impacted by the authors' patient selection choices and cost assumptions, especially with significantly cheaper oral alternatives. More data are needed to best identify the optimal therapeutic use for the long-acting lipoglycopeptides.

Limitations

The most significant limitation in this analysis was selection bias: 38 of 39 patients (97%) who received dalbavancin or oritavancin had a documented reason that described why OPAT therapy with traditional medications would not be optimal, including logistics, AEs, or clinical failures. Most patients treated with vancomycin and daptomycin were admitted into a SNF, RF, or CLC for the remainder of their treatment, allowing for closer monitoring and care compared to patients treated with oritavancin and dalbavancin, but at a greater cost. For patients sent to a community based SNF or RF, laboratory data were not available unless internally drawn or documented in the electronic medical record.

Additionally, not all cost data were available from VA sources; some were applied from literature, pharmacoeconomist, or best faith effort based on workflow. The cost data from third party contractors providing OPAT services to some BVAMC patients during the time frame of this analysis were not available. Due to its small sample size, outliers had the potential to affect averages reported and accuracy of the cost analysis. Emerging evidence suggests that daptomycin doses higher than the manufacturer-recommended regimen may be required for select indications, a factor that could affect cost, AEs, and efficacy outcomes.²⁸ The acquisition cost of oritavancin and dalbavancin may vary by institution (ie, VA contract prices vs non- VA contract prices) and change over time. A current assessment of cost is needed to best visualize institutional benefit.

Finally, while the patient demographic of this MUE was highly representative of the demographic treated at the BVAMC (males aged >65 years), it may not be applicable to external patient populations. This analysis evaluated off-label indications for these medications. Consequently, this analysis would likely not be applicable to non-VA institution, as third-party payers (eg, insurance) are unlikely to cover medications for off-label indications.

CONCLUSIONS

This study found cost savings associated with the use of oritavancin and dalbavancin compared with vancomycin and daptomycin, particularly for the treatment of osteomyelitis. As safety and efficacy data continues to emerge, the use of long-acting lipoglycopeptides appears to be an increasingly attractive alternative option compared to traditional outpatient antimicrobial therapy, depending on the structure of the program. Larger, multicenter cost-effectiveness studies are needed to further establish the impact of these novel agents.

Oritavancin and dalbavancin are long acting lipoglycopeptides indicated for the treatment of acute bacterial skin and skin structure infections (ABSSSI).^1,2 Largely due to their long half-lives, prolonged tissue concentrations at sites of infection, tolerability, and minimal requirement for therapeutic drug monitoring, these agents are attractive options in outpatient settings.^3,4 A 1- or 2-dose treatment of oritavancin and dalbavancin may be sufficient for conditions traditionally treated with outpatient parenteral antimicrobial therapy (OPAT) via peripherally inserted central catheter (PICC).

Limited research supports the use of dalbavancin and oritavancin for bone and joint infections, infective endocarditis, and bloodstream infections (BSIs). However, the US Food and Drug Administration has approved an indication for the treatment of ABSSSI.^3-9 Dosing for these off-label indications varies but typically consists of an initial intravenous (IV) dose (1000 mg, 1200 mg, or 1500 mg), with a subsequent dose 1 to 2 weeks later or administered once weekly.^6-10

Due in part to the recent availability of oritavancin and dalbavancin relative to the publication of practice guidelines, their appropriate place in therapy continues to evolve based on emerging literature.^11,12 One potential barrier of use for these medications is their cost. Based on the number of doses administered, the 2022 estimated total acquisition cost of therapy for oritavancin and dalbavancin was $1014 to $4397 and $3046 to $7150, respectively (eAppendix). Despite the high acquisition costs, these agents do not require the placement of an indwelling central line, can be administered in outpatient settings, and require minimal therapeutic dose monitoring compared to vancomycin.^13-15 This medication use evaluation (MUE) compared the total cost of treatment with oritavancin and dalbavancin vs therapies traditionally used for OPAT or prolonged IV inpatient therapy.

METHODS

This retrospective MUE was conducted at the Boise Veterans Affairs Medical Center (BVAMC), a level 2 facility with an extensive rural catchment area. BVAMC provides many OPAT services, including medications, supplies, and dressing changes after initial clinic or inpatient education. Contracted vendors may also assist with at-home nursing care using supplies provided by the BVAMC. Cases were identified using an internal database of OPAT patients and those who received oritavancin or dalbavancin between September 1, 2017, and November 1, 2022. Patients aged ≥ 18 years who received ≥ 1 dose of oritavancin or dalbavancin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were included. Comparator treatments consisting of ≥ 1 week of vancomycin or daptomycin for ABSSSI, osteomyelitis/joint infections, endocarditis, and BSI were identified through review of OPAT and Infectious Diseases service consults during the same timeframe. Patients were excluded if any antibiotic was prescribed by a non- VA clinician, if medications were not provided by OPAT, or if chart review did not identify an ABSSSI, osteomyelitis/ joint infection, or BSI diagnosis.

Electronic medical record review was conducted using a standardized data collection form (eAppendix). Data collected included demographics, infectious diagnosis, treatment administered, administration procedures and related visits and treatment locations, outcomes including clinical failure, adverse events (AEs), and hospital readmission.

Clinical failure was defined as readmission or death due to worsening infection or readmission secondary to a documented potential AE to the evaluated antibiotics within 90 days after initiation. Clinical failures excluded readmissions not associated with infection including comorbidities or elective procedures. AEs included new onset renal failure (serum creatinine ≥ 0.5 mg/dL), neutropenia (neutrophils ≤ 500), thrombocytopenia (platelets < 100,000), eosinophilia (> 15% eosinophils), or creatine phosphokinase > 10 times the upper limit of normal, and Clostridioides difficile (C. difficile) infection. Line complications included thrombophlebitis, local inflammation, or infection requiring line replacement (eAppendix).

A cost-minimization approach was used to assess the total cost of treatment.¹⁶ Patients who received oritavancin or dalbavancin were matched with patients that received vancomycin and daptomycin for the same indication and about 1 month of initiation through the randomization function in Microsoft Excel. This accounted for changes in personnel, nonformulary drug approvals, cost, and changes in practice during the pandemic. Costs were calculated using a decision tree as a base model (Figure 1). In this model, each treatment dyad was assessed for the presence or absence of clinical failure, adverse event (medication and line complications), and treatment setting endpoints. Cost estimates were tabulated for each patient that received treatment using published VA data, literature, pharmacoeconomist guidance, or best faith effort based on workflow. ^17-20 All cost estimates were based on 2022 figures or adjusted for inflation if obtained prior to 2022. Secondary endpoints of this analysis included estimated total cost of medication acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home-health services.

This evaluation was classified by the BVAMC Medication Use Evaluation research determination subcommittee as a quality improvement project and was considered exempt from VA Human Subjects Research requirements based on the VA Policy Handbook guideline 1058.05.

RESULTS

The study identified 44 patients who received dalbavancin or oritavancin between September 1, 2017, and October 31, 2022. Thirty-nine patients were included in the analysis: 24 received oritavancin and 15 received dalbavancin and were matched by indication to 10 patients who received vancomycin and 8 patients who received daptomycin. Three patients could not be matched by indication of ABSSSI (Figure 2). Most patients were male, aged > 65 years, and were treated for osteomyelitis (Table 1). No patients were treated for infective endocarditis. A myriad of concomitant antibiotics were used to treat patients and culture results indicated that most infections treated with oritavancin and dalbavancin were polymicrobial.

The mean total cost of therapy per patient receiving oritavancin, dalbavancin, vancomycin, and daptomycin was $35,630, $59,612, $73,333, and $73,708, respectively (Figure 3). When stratified by indication, 27 patients (69%) in the oritavancin/dalbavancin group were treated for osteomyelitis/ joint infections (16 oritavancin, 11 dalbavancin), 9 patients (23%) were treated for BSI (6 oritavancin, 3 dalbavancin), and 3 patients (8%) were treated for ABSSSI (2 oritavancin, 1 dalbavancin). The mean cost per patient for osteomyelitis/joint infections with oritavancin, dalbavancin, vancomycin, and daptomycin was $34,678, $54,224, $87,488, and $85,044, respectively. The mean cost per patient for BSI for oritavancin, dalbavancin, vancomycin, and daptomycin was $35,048, $75,349, $40,305, and $68,068, respectively. The mean cost per patient for ABSSSI for oritavancin and dalbavancin was $44,771 and $71,672.51.

Estimated total drug cost represents the cost of drug acquisition, administration supplies, laboratory monitoring, and human resources for OPAT visits or receiving home health services. The mean cost per patient of drug-related therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2203, $5924, $3637, and $7146, respectively (Table 2).

The mean cost per patient for osteomyelitis therapy for oritavancin, dalbavancin, vancomycin, and daptomycin was $2375, $6775, $4164, $8152, respectively. The mean cost of per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $1737, $3475, $2409, and $1016, respectively. The mean cost per patient for oritavancin and dalbavancin for ABSSSI treatment, was $1553 and $3910, respectively.

Setting-related costs include expenses from inpatient admissions and postdischarge stays at community living centers (CLCs), skilled nursing facilities (SNFs), or rehabilitation facilities (RFs) for the duration of antimicrobial therapy. The mean setting-related therapy cost for osteomyelitis treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $27,852, $17,815, $83,324, and $72,856, respectively. The mean setting-related therapy cost per patient for BSI treatment with oritavancin, dalbavancin, vancomycin, and daptomycin was $33,310, $60,668, $37,734, and $67,074, respectively. The mean setting-related therapy cost per patient for ABSSSI treatment for oritavancin and dalbavancin was $43,218 and $67,762.00, respectively.

Six of 39 patients (15%) had clinical failure: 2 patients with oritavancin and 4 patients with dalbavancin. Four patients were readmitted for worsening infection and 2 for AEs. One patient (13%) in the daptomycin group had clinical failure due to readmission for worsening infection. There was no clinical failure with vancomycin. The costs associated with clinical failure per patient for oritavancin, dalbavancin, vancomycin, and daptomycin were $2925, $23,972, $0, and $3601, respectively (Table 3).

Thirty-eight patients (97%) who received oritavancin or dalbavancin had difficulty adhering to vancomycin or daptomycin OPAT. Oritavancin or dalbavancin was used in 10 patients (26%) who lacked support at home and 15 patients (38%) who had either a contraindication or previous failure with other antimicrobials, which were the most common explanations.

DISCUSSION

Long-acting lipoglycopeptides represent a potential alternative to home IV therapy that can avoid prolonged IV access with traditional OPAT. This offers significant advantages, allowing patients to be discharged from the hospital early, especially in rural areas with little OPAT infrastructure or those with logistic challenges. In this analysis, treatment with oritavancin for osteomyelitis, BSI, or ABSSSI, yielded an estimated cost savings of about $37,000 per patient, compared to treatment of matched indications with vancomycin and daptomycin. For every patient treated with dalbavancin for osteomyelitis, BSI, or ABSSSI, the cost savings was about $13,000 per patient, compared to treatment of matched indications for daptomycin and vancomycin. The estimated cost savings per patient for oritavancin was similar to previously published projections ($30,500 to $55,831).¹⁵

Cost savings were primarily driven by setting-related costs. The greatest contrast between the oritavancin and dalbavancin group compared to the vancomycin and daptomycin group was the length of stay in a postdischarge CLC, SNF, or RF setting. This analysis estimated that for every patient treated with oritavancin for osteomyelitis, the setting-related cost savings per patient was about $55,000 compared with vancomycin, and about $45,000 per patient compared with daptomycin. Furthermore, the estimated setting-related cost savings for osteomyelitis treatment with dalbavancin was about $65,000 compared with vancomycin and about $55,000 compared with daptomycin.

Clinical failure occurred with greater frequency in the oritavancin and dalbavancin groups (15%), compared with the vancomycin (0%) and daptomycin (13%) groups. Although the clinical failure rates in patients with osteomyelitis treated with oritavancin and dalbavancin compared with daptomycin were like those in previously published research (10%-30%), the rates of clinical failure for vancomycin in this analysis were lower than those in the oritavancin and dalbavancin group.^8,21,22 The discrepancy in clinical failure rates between this analysis and previous research is likely due to selection bias. Based on the percentages of clinical failure found in the analysis, it is not surprising to note that the total clinical failure-related cost per patient was higher for oritavancin and dalbavancin compared to vancomycin, but similar between oritavancin and daptomycin.

This analysis also found that 15% of patients in the oritavancin and dalbavancin group experienced an AE compared to 10% of patients in the vancomycin group and none in the daptomycin group. In the oritavancin and dalbavancin group, the 2 most common AEs were infusion-related reactions and C. difficile colitis. Although infusion related reactions are easier to correspond to oritavancin and dalbavancin, it becomes difficult to definitively attribute the occurrence of C. difficile to these drugs as many patients were receiving concomitant antibiotics. Although not a primary or secondary objective, the rate of IV-line AEs were more prevalent in the vancomycin (10%), and daptomycin (13%) groups, compared to none in the oritavancin and dalbavancin group. This finding was expected; oritavancin and dalbavancin do not require a central IV line for administration.

Pharmacoeconomic literature continues to emerge with long-acting lipoglycopeptides. A 2024 Italian retrospective single-center analysis of 62 patients reported mean cost reductions > €3200 per patient (> $3400) given dalbavancin compared with the standard of care for ABSSSI or more deep-seeded infections such as osteomyelitis.²³ A 2023 Spanish observational multicenter analysis of 124 patients with infective endocarditis demonstrated high efficacy, safety and cost-effectiveness with dalbavancin vs conventional treatments, with a mean savings of > €5548 per patient (> $6200).²⁴ An analysis of the implementation of a dalbavancin order pathway for ABSSSI to avert inpatient admissions at 11 US emergency departments found a mean cost savings of $5133 per patient and $1211 per hospitalization day avoided, compared with inpatient usual care.²⁵

Conversely, a multicenter, retrospective study of 209 patients in a community-based health care system failed to show a financial benefit for dalbavancin use when compared to standard of care for ABSSSI with higher readmission rates.²⁶ Turco et al also reported increased cost results for 64 patients who received dalbavancin vs standard of care for ABSSSI.²⁷ These discordant findings in ABSSSI studies may be impacted by the authors' patient selection choices and cost assumptions, especially with significantly cheaper oral alternatives. More data are needed to best identify the optimal therapeutic use for the long-acting lipoglycopeptides.

Limitations

The most significant limitation in this analysis was selection bias: 38 of 39 patients (97%) who received dalbavancin or oritavancin had a documented reason that described why OPAT therapy with traditional medications would not be optimal, including logistics, AEs, or clinical failures. Most patients treated with vancomycin and daptomycin were admitted into a SNF, RF, or CLC for the remainder of their treatment, allowing for closer monitoring and care compared to patients treated with oritavancin and dalbavancin, but at a greater cost. For patients sent to a community based SNF or RF, laboratory data were not available unless internally drawn or documented in the electronic medical record.

Additionally, not all cost data were available from VA sources; some were applied from literature, pharmacoeconomist, or best faith effort based on workflow. The cost data from third party contractors providing OPAT services to some BVAMC patients during the time frame of this analysis were not available. Due to its small sample size, outliers had the potential to affect averages reported and accuracy of the cost analysis. Emerging evidence suggests that daptomycin doses higher than the manufacturer-recommended regimen may be required for select indications, a factor that could affect cost, AEs, and efficacy outcomes.²⁸ The acquisition cost of oritavancin and dalbavancin may vary by institution (ie, VA contract prices vs non- VA contract prices) and change over time. A current assessment of cost is needed to best visualize institutional benefit.

Finally, while the patient demographic of this MUE was highly representative of the demographic treated at the BVAMC (males aged >65 years), it may not be applicable to external patient populations. This analysis evaluated off-label indications for these medications. Consequently, this analysis would likely not be applicable to non-VA institution, as third-party payers (eg, insurance) are unlikely to cover medications for off-label indications.

CONCLUSIONS

This study found cost savings associated with the use of oritavancin and dalbavancin compared with vancomycin and daptomycin, particularly for the treatment of osteomyelitis. As safety and efficacy data continues to emerge, the use of long-acting lipoglycopeptides appears to be an increasingly attractive alternative option compared to traditional outpatient antimicrobial therapy, depending on the structure of the program. Larger, multicenter cost-effectiveness studies are needed to further establish the impact of these novel agents.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Excessive alcohol use is one of the leading preventable causes of death in the United States, responsible for about 178,000 deaths annually and an average of 488 daily deaths in 2020 and 2021.¹Alcohol-related deaths increased by 49% between 2006 and 2019.² This trend continued during the COVID-19 pandemic, with death certificates that listed alcohol increasing by > 25% from 2019 to 2020, and another 10% in 2021.³ This increase of alcohol-related deaths includes those as a direct result of chronic alcohol use, such as alcoholic cardiomyopathy, alcoholic hepatitis and cirrhosis, and alcohol-induced pancreatitis, as well as a result of acute use such as alcohol poisoning, suicide by exposure to alcohol, and alcohol-impaired driving fatalities.⁴

Excessive alcohol consumption poses other serious risks, including cases when intake is abruptly reduced without proper management. Alcohol withdrawal syndrome (AWS) can vary in severity, with potentially life-threatening complications such as hallucinations, seizures, and delirium tremens.⁵

These risks highlight the importance of professional intervention and support, not only to mitigate risks associated with AWS, but provide a pathway towards recovery from alcohol use disorder (AUD).

According to the 2022 National Survey on Drug Use and Health, 28.8 million US adults had AUD in the prior year, yet only 7.6% of these individuals received treatment and an even smaller group (2.2%) received medication-assisted treatment for alcohol.^6,7 This is despite American Psychiatric Association guidelines for the pharmacological treatment of patients with AUD, including the use of naltrexone, acamprosate, disulfiram, topiramate, or gabapentin, depending on therapy goals, past medication trials, medication contraindications, and patient preference.⁸ Several of these medications are approved by the US Food and Drug Administration (FDA) for the treatment of AUD and have support for effectiveness from randomized controlled trials and meta-analyses.^9-11

Clinical practice guidelines for the management of substance use disorders (SUDs) from the US Department of Veterans Affairs (VA) and US Department of Defense have strong recommendations for naltrexone and topiramate as first-line pharmacotherapies for moderate to severe AUD. Acamprosate and disulfiram are weak recommendations as alternative options. Gabapentin is a weak recommendation for cases where first-line treatments are contraindicated or ineffective. The guidelines emphasize the importance of a comprehensive approach to AUD treatment, including psychosocial interventions in addition to pharmacotherapy.¹²

A 2023 national survey found veterans reported higher alcohol consumption than nonveterans.¹³ At the end of fiscal year 2023, > 4.4 million veterans—6% of Veterans Health Administration patients—had been diagnosed with AUD.14 However, > 87% of these patients nationally, and 88% of Veterans Integrated Service Network (VISN) 21 patients, were not receiving naltrexone, acamprosate, disulfiram, or topiramate as part of their treatment. The VA Academic Detailing Service (ADS) now includes AUD pharmacotherapy as a campaign focus, highlighting its importance. The ADS is a pharmacy educational outreach program that uses unbiased clinical guidelines to promote aligning prescribing behavior with best practices. Academic detailing methods include speaking with health care practitioners (HCPs), and direct-to-consumer (DTC) patient education.

ADS campaigns include DTC educational handouts. Past ADS projects and research using DTC have demonstrated a significant improvement in outcomes and positively influencing patients’ pharmacotherapy treatment. ^15,16 A VA quality improvement project found a positive correlation between the initiation of AUD pharmacotherapy and engagement with mental health care following the distribution of AUD DTC patient education. ¹⁷ This project aimed to apply the same principles of prior research to explore the use of DTC across multiple facilities within VISN 21 to increase AUD pharmacotherapy. VISN 21 includes VA facilities and clinics across the Pacific Islands, Nevada, and California and serves about 350,000 veterans.

METHODS

A prospective cohort of VISN 21 veterans with or at high risk for AUD was identified using the VA ADS AUD Dashboard. The cohort included those not on acamprosate, disulfiram, naltrexone, topiramate, or gabapentin for treatment of AUD and had an elevated Alcohol Use Disorder Identification Test-Consumption (AUDIT-C) score of ≥ 6 (high risk) with an AUD diagnosis or ≥ 8 (severe risk) without a diagnosis. The AUDIT-C scores used in the dashboard are supported by the VA AUD clinician guide as the minimum scores when AUD pharmacotherapy should be offered to patients.¹⁸ Prescriptions filled outside the VA were not included in this dashboard.

Data and patient information were collected using the VA Corporate Data Warehouse. To be eligible, veterans needed a valid mailing address within the VISN 21 region and a primary care, mental health, or SUD clinician prescriber visit scheduled between October 1, 2023, and January 31, 2024. Veterans were excluded if they were in hospice, had a 1-year mortality risk score > 50% based on their Care Assessment Need (CAN) score, or facility leadership opted out of project involvement. Patients with both severe renal and hepatic impairments were excluded because they were ineligible for AUD pharmacotherapy. However, veterans with either renal or hepatic impairment (but not both) were included, as they could be potential candidates for ≥ 1 AUD pharmacotherapy option.

Initial correspondence with facilities was initiated through local academic detailers. A local champion was identified for the 1 facility without an academic detailer. Facilities could opt in or out of the project. Approval was provided by the local pharmacy and therapeutics committee, pharmacy, primary care, or psychiatry leadership. Approval process and clinician involvement varied by site.

Education

The selected AUD patient education was designed and approved by the national VA ADS (eappendix). The DTC patient education provided general knowledge about alcohol, including what constitutes a standard amount of alcohol, what is considered heavy drinking, risks of heavy drinking, creating a plan with a clinician to reduce and manage withdrawal symptoms, and additional resources. The DTC was accompanied by a cover letter that included a local facility contact number.

A centralized mailing facility was used for all materials. VA Northern California Health Care System provided the funding to cover the cost of postage. The list of veterans to be contacted was updated on a rolling basis and DTC education was mailed 2 weeks prior to their scheduled prescriber visit.

The eligible cohort of 1260 veterans received DTC education. A comparator group of 2048 veterans that did not receive DTC education was obtained retrospectively by using the same inclusion and exclusion criteria with a scheduled primary care, mental health, or SUD HCP visit from October 1, 2022, to January 31, 2023. The outcomes assessed were within 30 days of the scheduled visit, with the primary outcome as the initiation of AUD-related pharmacotherapy and the secondary outcome as the placement of a consultation for mental health or SUD services. Any consultations sent to Behavioral Health, Addiction, Mental Health, Psychiatric, and SUD services following the HCP visit, within the specified time frame, were used for the secondary outcome.

Matching and Analysis

A 1-to-1 nearest neighbor propensity score (PS) matching without replacement was used to pair the 1260 veterans from the intervention group with similarly scored comparator group veterans for a PS-matched final dataset of 2520 veterans. The PS model was a multivariate logistic regression with the outcome being exposure and comparator group status. Baseline characteristics used in the PS model were age, birth sex, race, facility of care, baseline AUDIT-C score, and days between project start and scheduled appointment. Covariate imbalance for the PS-matched sample was assessed to ensure the standardized mean difference for all covariates fell under a 0.1 threshold (Figure).¹⁹

A frequency table was provided to compare the discrete distributions of the baseline characteristics in the intervention and comparator groups. Logistic regression analysis was performed to evaluate the association between DTC education exposure and pharmacotherapy initiation, while controlling for potential confounders. Univariate and multivariate P value results for each variable included in the model were reported along with the multivariate odds ratios (ORs) and their associated 95% CIs. Logistic regression analyses were run for both outcomes. Each model included the exposure and comparator group status as well as the baseline characteristics included in the PS model. Statistical significance was set at P < .05. All statistical analyses were performed with R version 4.2.1.

RESULTS

Two of 7 VISN 21 sites did not participate, and 3 had restrictions on participation. DTC education was mailed about 2 weeks prior to scheduled visit for 1260 veterans; 53.6% identified as White, 37.6% were aged 41 to 60 years, and 79.2% had an AUDIT-C ≥ 8 (Table 1). Of those mailed education, there were 173 no-show appointments (13.7%). Thirty-two veterans (2.5%) in the DTC group and 33 veterans (2.6%) in the comparator group received an AUD-related pharmacotherapy prescription (P = .88) (Table 2). One hundred seventy-one veterans (13.6%) in the DTC group and 160 veterans (12.7%) in the comparator group had a consult placed for mental health or SUD services within 30 days of their appointment (P = .59) (Table 3).

DISCUSSION

This project did not yield statistically significant differences in either the primary or secondary outcomes within the 30-day follow-up window and found limited impact from the DTC educational outreach to veterans. The percentage of veterans that received AUD-related pharmacotherapy or consultations for mental health or SUD services was similarly low in the DTC and comparator groups. These findings suggest that although DTC education may raise awareness, it may not be sufficient on its own to drive changes in prescribing behavior or referral patterns without system-level support.

Addiction is a complex disease faced with stigma and requiring readiness by both the HCP and patient to move forward in support and treatment. The consequences of stigma can be severe: the more stigma perceived by a person with AUD, the less likely they are to seek treatment.²⁰ Stigma may exist even within HCPs and may lead to compromised care including shortened visits, less engagement, and less empathy.¹⁹ Cultural attitude towards alcohol use and intoxication can also be influenced through a wide range of sources including social media, movies, music, and television. Studies have shown targeted alcohol marketing may result in the development of positive beliefs about drinking and expand environments where alcohol use is socially acceptable and encouraged.²¹ These factors can impact drinking behavior, including the onset of drinking, binge drinking, and increased alcohol consumption.²²

Three VISN 21 sites in this study had restrictions on or excluded primary care from participation. Leadership at some of these facilities were concerned that primary care teams did not have the bandwidth to take on additional items and/or there was variable primary care readiness for initiating AUD pharmacotherapy. Further attempts should be made to integrate primary care into the process of initiating AUD treatment as significant research suggests that integrated care models for AUD may be associated with improved process and outcome measures of care.²³

There are several differences between this quality improvement project and prior research investigating the impact of DTC education for other conditions, such as the EMPOWER randomized controlled trial and VISN 22 project, which both demonstrated effectiveness of DTC education for reducing benzodiazepine use in geriatric veterans. ^15,16 These studies focused on reducing or stopping pharmacotherapy use, whereas this project sought to promote the initiation of AUD pharmacotherapy. These studies evaluated outcomes at least 6 months postindex date, whereas this project evaluated outcomes within 30 days postappointment. Furthermore, the educational content varied significantly. Other projects provided patients with information focused on specific medications and interventions, such as benzodiazepine tapering, while this project mailed general information on heavy drinking, its risks, and strategies for cutting back, without mentioning pharmacotherapy. The DTC material used in this project was chosen because it was a preapproved national VA ADS resource, which expedited the project timeline by avoiding the need for additional approvals at each participating site. These differences may impact the observed effectiveness of DTC education in this project, especially regarding the primary outcome.

Strengths and Limitations

This quality improvement project sent a large sample of veterans DTC education in a clinical setting across multiple sites. Additionally, PS matching methods were used to balance covariates between the comparator and DTC education group, thereby simulating a randomized controlled trial and reducing selection bias. The project brought attention to the VISN 21 AUD treatment rates, stimulated conversation across sites about available treatments and resources for AUD, and sparked collaboration between academic detailing, mental health, and primary care services. The time frame for visits was selected during the winter; the National Institute on Alcohol Abuse and Alcoholism notes this is a time when people may be more likely to engage in excessive alcohol consumption than at other times of the year.²⁴

The 30-day time frame for outcomes may have been too short to observe changes in prescribing or referral patterns. Additionally, the comparator group was comprised of veterans seen from October 1, 2022, to January 31, 2023, where seasonal timing may have influenced alcohol consumption behaviors and skewed the results. There were also no-show appointments in the DTC education group (13.7%), though it is likely some patients rescheduled and still received AUD pharmacotherapy within 30 days of the original appointment. Finally, it was not possible to confirm whether a patient opened and read the education that was mailed to them. This may be another reason to explore electronic distribution of DTC education. This all may have contributed to the lack of statistically significant differences in both the primary and secondary outcomes.

There was a high level of variability between facility participation in the project. Two of 7 sites did not participate, and 3 sites restricted primary care engagement. This represents a significant limitation, particularly for the secondary outcome of placing consultations for MH or SUD services. Facilities that only included mental health or SUD HCPs may have resulted in lower consultation rates due to their inherent specialization, reducing the likelihood of self-referrals.

The project may overestimate prescribed AUD pharmacotherapy in the primary outcome due to potential misclassification of medications. While the project adhered to the national VA ADS AUD dashboard’s definition of AUD pharmacotherapy, including acamprosate, disulfiram, naltrexone, topiramate, and gabapentin, some of these medications have multiple indications. For example, gabapentin is commonly prescribed for peripheral neuropathy, and topiramate is used to treat migraines and seizures. The multipurpose use adds uncertainty about whether they were prescribed specifically for AUD treatment, especially in cases where the HCP is responsible for treating a broad range of disease states, as in primary care.

CONCLUSIONS

Results of this quality improvement project did not show a statistically significant difference between patients sent DTC education and the comparator group for the initiation of AUD pharmacotherapy or placement of a consult to mental health or SUD services within 30 days of their scheduled visit. Future studies may seek to implement stricter criteria to confirm the intended use of topiramate and gabapentin, such as looking for keywords in the prescription instructions for use, performing chart reviews, and/or only including these medications if prescribed by a mental health or SUD HCP. Alternatively, future studies may consider limiting the analysis to only FDA-approved AUD medications: acamprosate, disulfiram, and naltrexone. It is vital to continue to enhance primary care HCP readiness to treat AUD, given the existing relationships and trust they often have with patients. Electronic methods for distributing DTC education could also be advantageous, as these methods may have the ability to track whether a message has been opened and read. Despite a lack of statistical significance, this project sparked crucial conversations and collaboration around AUD, available treatments, and addressing potential barriers to connecting patients to care within VISN 21.

Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.^1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.⁴

Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.⁵

Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.⁶ These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.

PHASER

In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).^7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.⁹ The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.

Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.¹⁰

Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.¹¹ Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.¹² Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.¹³

Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.^12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.¹⁵ CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.

Gap Analysis

Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.¹⁶ Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.

CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs. 

In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.

Process Improvement Data

The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.

Discussion

Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.

Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.

Conclusions

A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.

Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.^1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.⁴

Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.⁵

Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.⁶ These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.

PHASER

In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).^7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.⁹ The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.

Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.¹⁰

Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.¹¹ Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.¹² Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.¹³

Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.^12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.¹⁵ CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.

Gap Analysis

Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.¹⁶ Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.

CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs. 

In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.

Process Improvement Data

The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.

Discussion

Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.

Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.

Conclusions

A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.

Peer-review, evidence-based, detailed gene/drug clinical practice guidelines suggest that genetic variations can impact how individuals metabolize medications, which is sometimes included in medication prescribing information.^1-3 Pharmacogenomic testing identifies genetic markers so medication selection and dosing can be tailored to each individual by identifying whether a specific medication is likely to be safe and effective prior to prescribing.⁴

Pharmacogenomics can be a valuable tool for personalizing medicine but has had suboptimal implementation since its discovery. The US Department of Veterans Affairs (VA) health care system reviewed the implementation of the Pharmacogenomic Testing for Veterans (PHASER) program. This review identified clinician barriers pre- and post-PHASER program implementation; staffing issues, competing clinical priorities, and inadequate PHASER program resources were the most frequently reported barriers to implementation of pharmacogenomic testing.⁵

Another evaluation of the implementation of the PHASER program that surveyed VA patients found that patients could be separated into 3 groups. Acceptors of pharmacogenomic testing emphasized potential health benefits of testing. Patients that declined testing often cited concerns for genetic information affecting insurance coverage, being misused, or being susceptible to data breach. The third group—identified as contemplators—reported the need for clinician outreach to impact their decision on whether or not to receive pharmacogenomic testing.⁶ These studies suggest that removing barriers by providing ample pharmacogenomics resources to clinicians, in addition to detailed training on how to offer and follow up with patients regarding pharmacogenomic testing, is crucial to successful implementation of the PHASER program.

PHASER

In 2019, the VA began working with Sanford Health to establish the PHASER program and offer pharmacogenomic testing. PHASER has since expanded to 25 VA medical centers, including the VA Central Ohio Healthcare System (VACOHCS).^7,8 Pharmacogenomic testing through PHASER is conducted using a standardized laboratory panel that includes 12 different medication classes.⁹ The drug classes include certain anti-infective, anticoagulant, antiplatelet, cardiovascular, cholesterol, gastrointestinal, mental health, neurological, oncology, pain, transplant, and other miscellaneous medications. Medications are correlated to each class and assessed for therapeutic impacts based on gene panel results.

Clinical recommendations for medication-gene interactions can range from monitoring for increased risk of adverse effects or therapeutic failure to recommending avoiding a medication. For example, patients who test positive for the HLA-B gene have significantly increased risk of hypersensitivity to abacavir, an HIV treatment.¹⁰

Similarly, patients who cannot adequately metabolize cytochrome P450 2C19 should consider avoiding clopidogrel as they are unlikely to convert clopidogrel to its active prodrug, which reduces its effectiveness.¹¹ Pharmacists can play a critical role educating patients about pharmacogenomic testing, especially within hematology and oncology.¹² Patients can benefit from this testing even if they are not currently taking medications with known concerns as they could be prescribed in the future. The SLCO1B1 gene-drug test, for example, can identify risk for statin-associated muscle symptoms.¹³

Clinical pharmacist practitioners (CPPs) can increase access to genetic testing because they interact with patients in a variety of settings and can order this laboratory test.^12,14 Recent research has demonstrated that most VA patients carry ≥ 1 genetic variant that may influence medication decisions and that half of veterans are prescribed a medication with known gene-drug interactions.¹⁵ CPP ordering of pharmacogenomic tests at the VACOHCS outpatient clinic was evaluated through collection of baseline data from March 8, 2023, to September 8, 2023. A goal was identified to increase orders by 50% for a patient care quality improvement initiative and use CPPs to increase access to pharmacogenomic testing. The purpose of this quality improvement initiative was to expand access to pharmacogenomic testing through process implementation and improvement within CPP-led clinic settings.

Gap Analysis

Lean Six Sigma A3 methodology was used to identify ways to increase the use of pharmacogenomic testing for veterans at VACOHCS and develop an improved process for increased ordering of pharmacogenomic testing. Lean Six Sigma A3 methodology is a stepwise approach to process improvement that helps identify gaps in efficiency, sustainable changes, and eliminate waste.¹⁶ Baseline data were collected from March 8, 2023, to September 8, 2023, to determine the frequency of CPPs ordering pharmacogenomic laboratory panels during clinic appointments. The ordering of pharmacogenomic panels was monitored by the VACOHCS PHASER coordinator.

CPPs were surveyed to identify perceived barriers to PHASER implementation. A gap analysis was conducted using Lean Six Sigma A3 methodology. Gap analyses use lean tools such as a Fishbone Diagram to illustrate and identify the gap between current state and ideal state. (Figure 1).The following barriers were identified: lack of clinician education materials, lack of a standardized patient screening process, time constraints on patient education and ordering, higher priority clinical needs, forgetting to order, lack of comfort with pharmacogenomics ordering and education, lack of support for the initiative, and increased workload and burnout. Among these perceived barriers, higher priority clinical needs, forgetting to order, and time constraints ranked highest in importance among CPPs. 

In line with Lean Six Sigma A3 methodology, several tests of change were used to improve pharmacogenomic testing ordering. These changes focused on increasing patient and clinician awareness, facilitating discussion, educating clinicians, and simplifying documentation to ease time constraints. Several strategies were employed postimplementation (Figure 2). Prefilled templates simplified documentation. These templates helped identify patients without pharmacogenomic testing, provided reminders, and saved documentation time during visits. CPPs also received training and materials on PHASER ordering and documentation within encounter notes. Additionally, patient-directed advertisements were displayed in CPP examination rooms to help inspire and facilitate discussion between veterans and CPPs.

Process Improvement Data

The quality improvement project goal was to increase PHASER orders by 50% after 3 months. PHASER orders increased from 87 at baseline (March 8, 2023, to September 8, 2023) to 196 during the intervention (November 16, 2023, to February 16, 2024), a 125% increase. Changes were consistent and sustained with 65 orders the first month, 67 orders the second month, and 64 orders the third month.

Discussion

Using Lean Six Sigma A3 methodology for a quality improvement process to increase PHASER orders by CPPs revealed barriers and guided potential solutions to overcome these barriers. Interventions included additional CPP training and ordering, tools for easier identification of potential patients, documentation best practices, patient-directed advertisements to facilitate conversations. These interventions required about 8 hours for preparation, distribution, development, and interpretation of surveys, education, and documentation materials. The financial impact of these interventions was already included in allotted office materials budgeted and provided. Additional funding was not needed to provide patient-directed advertisements or education materials. The VACOHCS pharmacogenomics CPP discusses PHASER test results with patients at a separate appointment.

Future directions include educating other CPPs to assist in discussing results with veterans. Overall, the changes implemented to improve the PHASER ordering process were low effort and exemplify the ease of streamlining future initiatives, allowing for sustained optimal implementation of pharmacogenomic testing.

Conclusions

A quality improvement initiative resulted in increased PHASER orders and a clearly defined process, allowing for a continued increase and sustained support. Perceived barriers were identified, and the changes implemented were often low effort but exhibited a sustained impact. The insights gleaned from this process will shape future process development initiatives and continue to sustain pharmacogenomic testing ordering by CPPs. This process will be extended to other VACOHCS clinical departments to further support increased access to pharmacogenomic testing, reduce medication trial and error, and reduce hospitalizations from adverse effects for veterans.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

Osteoporotic fragility fractures constitute a significant public health concern, with 1 in 2 women and 1 in 5 men aged > 50 years sustaining an osteoporotic fracture.¹ Osteoporotic fractures are costly and associated with reduced quality of life and impaired survival.^2-6 Many interventions including fall mitigation, calcium, vitamin D supplementation, and osteoporosis—specific medications reduce fracture risk.⁷ New medications for treating osteoporosis, including anabolic therapies, are costly and require clinical oversight to ensure safe delivery. This includes laboratory monitoring, timing of in-clinic dosing and provision of sequence therapy.^8,9 COVID-19 introduced numerous barriers to osteoporosis care, raising concerns for medication interruption and patients lost to follow-up, which made monitoring these high risk and costly medications even more important.

The US Department of Veterans Affairs (VA) was an early adopter of using the electronic health record to analyze and implement system-wide processes for population management and quality improvement.¹⁰ This enabled the creation of clinical dashboards to display key performance indicator data that support quality improvement and patient care initiatives.^11-15 The VA Puget Sound Health Care System (VAPSHCS) has a dedicated osteoporosis clinic focused on preventing and treating veterans at high risk for fracture. Considering the growing utilization of osteoporosis medications, particularly those requiring timed sequential therapy to prevent bone mineral density loss and rebound osteoporotic fractures, close monitoring and follow-up is required. The COVID-19 pandemic made clear the need for proactive osteoporosis management. This article describes the creation and use of an automated clinic dashboard to identify and contact veterans with osteoporosis-related care needs, such as prescription refills, laboratory tests, and clinical visits.

Methods

An automated dashboard was created in partnership with VA pharmacy clinical informatics to display the osteoporosis medication prescription (including last refill), monitoring laboratory test values and most recent osteoporosis clinic visit for each clinic patient. Data from the VA Corporate Data Warehouse were extracted. The resulting tables were used to create a patient cohort with ≥ 1 active medication for alendronate, zoledronic acid, the parathyroid hormone analogues (PTH) teriparatide or abaloparatide, denosumab, or romosozumab. Notably, alendronate was the only oral bisphosphonate prescribed in the clinic. These data were formatted and displayed using Microsoft SQL Server Reporting Services. The secure and encrypted dashboard alerts the clinic staff when prescriptions, appointments, or laboratory tests, such as estimated glomerular filtration rate, 25-hydroxy vitamin D, calcium, and PTH are overdue or out of reference range. The dashboard tracked the most recent clinic visit or dual-energy X-ray absorptiometry (DXA) scan if performed within the VA. Overdue laboratory test alerts for bisphosphonates were flagged if delayed 12 months and 6 months for all other medications.

On March 20, 2021, the VAPSHCS osteoporosis clinic was staffed by 1 endocrinologist, 1 geriatrician, 1 rheumatologist, and 1 registered nurse (RN) coordinator. Overdue or out-of-range alerts were reviewed weekly by the RN coordinator, who addressed alerts. For any overdue laboratory work or prescription refills, the RN coordinator alerted the primary osteoporosis physician via the electronic health record for updated orders. Patients were contacted by phone to schedule a clinic visit, complete ordered laboratory work, or discuss osteoporosis medication refills based on the need identified by the dashboard. A letter was mailed to the patient requesting they contact the osteoporosis clinic for patients who could not be reached by phone after 2 attempts. If 3 attempts (2 phone calls and a letter) were unsuccessful, the osteoporosis physician was alerted so they could either call the patient, alert the primary referring clinician, or discontinue the osteoporosis medication.

Results

As of March 20, 2021, 139 patients were included on the dashboard. Ninety-two patients (66%) had unmet care needs and 29% were female. Ages ranged from 40 to 100 years (Table). The dashboard alerted the team to 3 patients lost to follow-up, all of whom had transferred to care outside the clinic. Twenty-three patients (17%) had overdue medications, including 2 (9%) who had not refilled oral bisphosphonate and 18 (78%) who were overdue for intravenous bisphosphonate treatment. One veteran flagged as overdue for their denosumab injection was unable to receive it due to a significant change in health status. Two veterans were overdue for a PTH analogue refill, 1 of whom had completed their course and transitioned to bisphosphonate.

The most common alert was 40 patients (29%) with overdue laboratory tests, 37 of which were receiving bisphosphonates. One patient included on the dashboard was taking romosozumab and all their monitoring parameters were up to date, thus their data were not included in the Table to prevent possible identification.

Discussion

A dashboard alerted the osteoporosis clinic team to veterans who were overdue for visits, laboratory work, and prescription renewals. Overall, 92 patients (66%) had unmet care needs identified by the dashboard, all of which were addressed with phone calls and/or letters. Most of the overdue medication refills and laboratory tests were for patients taking bisphosphonates avoiding VAPSHCS during the COVID-19 pandemic. The dashboard enabled the RN coordinator to promptly contact the patient, facilitate coordination of care requirements, and guarantee the safe and efficient delivery of osteoporosis care.

The VA has historically been a leader in the creation of clinical dashboards to support health campaigns.^11,12 These dashboards have successfully improved quality metrics towards the treatment of hepatitis C virus, heart failure, and highrisk opioid prescribing.^13-15 Data have shown that successful clinical dashboard implementation must be done in conjunction with protected time or staff to support care improvements.¹⁶ Additionally, the time required for clinical dashboards can limit their sustainability and feasibility.¹⁷ A study aimed at improving osteoporosis care for patients with Parkinson disease found that weekly multidisciplinary review of at-risk patients resulted in all new patients and 91% of follow-up patients receiving evidence- based osteoporosis treatments.¹⁷ However, despite the benefits, the intervention required significant time and resources. In contrast, the osteoporosis dashboard implemented at VAPSHCS was not time or resource intensive, requiring about 1 hour per week for the RN coordinator to review the dashboard and coordinate patient care needs.

Limitations

This study setting is unique from other health care organizations or VA health care systems. Implementation of a similar dashboard in other clinical settings where patients receive medical care in multiple health care systems may differ. The VA dedicates resources to support veteran population health management, which may not be available in other health care systems.^11,12 These issues may pose a barrier to implementing a similar osteoporosis dashboard in non-VA facilities. In addition, it is significant that while the dashboard can be reconfigured and adapted to track veterans across different VA facilities, certain complexities arise if essential data, such as laboratory tests and DXA imaging, are conducted outside of VA facilities. In such cases, manual entry of this information into the dashboard would be necessary. Because the dashboard was quickly developed during the COVID-19 pandemic, this study lacked preimplementation data on laboratory testing, medication refills, and DXA imaging, which would have enabled a comparison of adherence before and after dashboard implementation. Finally, we acknowledge the delay in publishing these findings; however, we believe sharing innovative approaches to providing care for high-risk populations is essential, as demonstrated during the COVID-19 pandemic.

Conclusions

An osteoporosis clinic dashboard served as a valuable clinical support tool to ensure safe and effective osteoporosis medication delivery at VAPSHCS. Considering the growing utilization of osteoporosis medications, this dashboard plays a vital role in facilitating care coordination for patients receiving these high-risk treatments.¹⁸ Use of the dashboard supported the effective use of high-cost osteoporosis medications and is likely to improve clinical osteoporosis outcomes.

Despite the known fracture risk reduction, osteoporosis medication adherence is low.^19,20 Maintaining consistent pharmacotherapy for osteoporosis is essential not only for fracture prevention but also reducing health care costs related to osteoporosis and preserving patient independence and functionality.^21-24 While initially developed in response to the COVID-19 pandemic, the dashboard remains useful. The VAPSHCS osteoporosis clinic is now staffed by 2 physicians (endocrine and rheumatology) and the dashboard is still in use. The RN coordinator spends about 15 minutes per week using the dashboard and managing the 67 veterans on osteoporosis therapy. This dashboard represents a sustainable clinical tool with the capacity to minimize osteoporosis care gaps and improve outcomes.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.

The impact of obesity in the United States is significant. Between August 2021 and August 2023, the prevalence of obesity (body mass index ≥ 30) in US adults was 40.3%.¹ The prevalence of obesity in adults aged 40 to 59 years was 46.4%, higher than the prevalence in adults aged 20 to 39 years (35.5%) and those aged ≥ 60 years (38.9%).¹ The excess annual medical costs associated with obesity in the US are estimated at nearly $173 billion.²

The first-line treatment for obesity is lifestyle modifications, including a healthy diet and exercise. When lifestyle modifications are not enough to achieve weight-loss goals, bariatric surgery and anti-obesity medications (AOMs) are often considered. Five medications were approved for the long-term tretament of obesity by the US Food and Drug Administration (FDA) between 2021 and 2023, when this study was conducted: semaglutide (Wegovy), liraglutide (Saxenda), phentermine and topiramate, naltrexone and bupropion, and orlistat. The clinically meaningful (and commonly accepted) weight-loss target for these medications is ≥ 5% from baseline by week 12 of the maximally tolerated dose of therapy. A 5% weight loss has been shown to be clinically significant in improving cardiometabolic risk factors.^3,4 These medications are intended to be used as an adjunct to healthy diet and exercise. Of note, semaglutide and liraglutide carry brand names, which are associated with different dosing for the treatment of type 2 diabetes mellitus (T2DM).

All 5 FDA-approved AOMs were available at the Veterans Affairs Sioux Falls Health Care System (VASFHCS) for the treatment of obesity at the time of the study. To qualify for an AOM, a veteran at VASFHCS must first work with a dietitian or be enrolled in the MOVE! clinic to participate in the weight management program, which focuses on dietary, exercise, and behavioral changes. At VASFHCS, AOMs are prescribed by primary care practitioners, clinical pharmacy providers, and advanced practitioners within the MOVE! program.

Ample data exist for the efficacy of AOMs. However, no published research has reported on AOM efficacy by age group (Appendix).^5-11 While most of the AOM clinical trials included older adults, the average age of participants was typically between 40 and 50 years. It is well-known that pharmacokinetic and pharmacodynamic changes occur as age increases. Renal and hepatic clearance is reduced while the volume distribution and sensitivities to some medications may increase. ¹² Although this study did not focus on specific pharmacokinetic and pharmacodynamic changes with respect to AOM, it is important to recognize that this may play a role in the efficacy and safety of AOMs in older adults.

Methods

This retrospective single-center chart review was performed using the VASFHCS Computerized Patient Record System to compare the efficacy of AOMs in older adults (aged ≥ 65 years) vs adults (aged < 65 years). The primary endpoint was the percent change in body weight from baseline to 6 and 12 months after initiation of AOM therapy in the older adult vs adult population. Secondary endpoints included changes in low-density lipoprotein (LDL), hemoglobin A_1c (HbA_1c), and blood pressure (BP) from baseline compared to 12 months on AOM therapy. HbA_1c was assessed in patients with T2DM or prediabetes at the time of AOM initiation. Two safety endpoints were also explored to determine the incidence of medication adverse events (AEs) and subsequent discontinuation of AOM. A subset analysis was performed to determine whether there was a difference in percent change in body weight between patients in 3 age groups: 18 to 40 years, 41 to 64 years, and ≥ 65 years.

The study population included patients who were prescribed an AOM between January 1, 2021, and June 30, 2023. Patients were excluded if they did not continue AOM therapy for ≥ 6 months after initiation or if they underwent gastric bypass surgery while undergoing AOM therapy. Patients taking semaglutide (Ozempic) or liraglutide (Victoza) for both T2DM and weight loss who were eventually switched to the weight loss formulations (Wegovy or Saxenda) were included. Patients who switched between semaglutide and liraglutide for weight loss were also included. Those taking semaglutide or liraglutide solely for T2DM treatment were excluded because they are dosed differently.

Collected data included age, gender, race, weight (baseline, 6 and 12 months after initiation of AOM), metabolic laboratory values/vital signs (HbA_1c, LDL, and BP at baseline and 12 months after initiation of AOM), diagnosis of T2DM or prediabetes, reported AEs associated with AOM therapy, and date of AOM initiation and discontinuation (if applicable). Baseline values were defined at the time of medication initiation or values documented within 6 months prior to medication initiation if true baseline data were not reported. If values were not recorded at months 6 and 12 after AOM initiation, values documented closest to those targets were used. Weights were used for baseline, 6-, and 12-month data unless they were unavailable due to use of virtual care modalities. In these cases, patient-reported weights were used. Patients were included in the 6-month data, but not the 12-month data, if they were taking AOMs for > 6 months but not for 12 months. If patients had been on multiple AOMs, baseline data were recorded at the start of the first medication that was used for 6 months or longer. Twelve-month data were recorded after subsequent medication change. Twelve-month metabolic laboratory values/vital signs were recorded for patients included in the study even if they did not complete ≥ 12 months of AOM therapy.

Statistical Analysis

Data from patients who were prescribed an AOM from January 2021 to June 2023 and who remained on the medication for ≥ 6 months were analyzed. Baseline characteristics were analyzed using descriptive statistics. The primary and secondary endpoints were evaluated using the t test. The safety endpoints were analyzed using descriptive statistics. An analysis of variance test was used for the subset analysis. Results with P < .05 were statistically significant.

Results

A total of 144 participants were included in this study, 116 in the adult group (aged < 65 years) and 28 in the older adult group (aged ≥ 65 years). Sixty-seven patients were excluded due to prespecified inclusion and exclusion criteria.

Other than the predetermined mean age differences (48 years vs 71 years), there were multiple differences in patient baseline characteristics. When comparing older adults and adults, average weight (283 lb vs 269 lb) and White race (89% vs 87%) were slightly higher in the older adult group. Also, a higher prevalence of T2DM (54% and 18%) and a lower prevalence of prediabetes (21% and 33%) was noted in the older adult group. HbA_1c and BP were similar between both groups at baseline, while LDL was slightly lower in the older adult group (Table 1).

Patients in the adult group lost a mean 7.0% and 8.7% of body weight at 6 and 12 months, respectively, while the older adult group lost 5.0% and 6.6% body weight at 6 and 12 months, respectively. The difference in percent change in body weight was not statistically different at 6 (P = .08) or 12 (P = .26) months between patients in the adult group vs the older adult group or in the specific age groups (18-40 years, 41-64 years, ≥ 65 years) at 6 months (P = .24) or 12 months (P = .53) (Figure).

At 12 months, the difference between the adult group vs the older adult group was not statistically significant for HbA_1c in patients with T2DM or prediabetes (P = .73), LDL (P = .95), systolic BP (P = .58), or diastolic BP (P = .51) (Table 2).

For the safety endpoint, the incidence of AEs was found to be different between groups. There were more reported AEs (61.2% vs 39.3%) and a greater increase in therapy discontinuation due to AEs (6.0% vs 0%) in the adult group compared to the older adult group (Table 3).

Discussion

Patients taking AOMs revealed no statistically significant difference in percent change in body weight at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. The subset analysis also showed no statistically significant difference in change in percent body weight between more narrowly defined age groups of 18 to 40 years, 41 to 64 years, and ≥ 65 years. This suggests that AOM may have similar efficacy for weight loss in all ages of adults.

Secondary endpoint findings showed no statistically significant difference in HbA_1c (in patients with T2DM or prediabetes), LDL, or BP at 12 months between the 2 groups. Although this study did not differentiate secondary outcomes based on the individual AOM, the change in HbA_1c in both groups was expected, given that 70% of the patients included in this study were taking a glucagon-like peptide-1 agonist (liraglutide and semaglutide) at some point during the study. It’s also worth noting that secondary endpoints were collected for patients who discontinued the AOM between 6 and 12 months. Therefore, the patients’ HbA_1c, LDL, and BP may not have accurately reflected the change that could have been expected if they had continued AOM therapy beyond the 12-month period.

Due to the different mechanisms and range in efficacy that AOMs have in regard to weight loss, changes in all outcomes, including weight, HbA_1c, LDL, and BP were expected to vary as patients were included even after switching AOM (collection of data started after ≥ 6 months on a single AOM). Switching of AOM after the first 6 months of therapy was recorded in 25% of the patients in the ≥ 65 years group and 330% of the patients in the < 65 years group.

The incidence of AEs and subsequent discontinuation of AOMs in this study was higher in the adult group. This study excluded patients who did not continue taking an AOM for at least 6 months. As a result, the incidence of AEs between the 2 groups within the first 6 months of AOM therapy remains unknown. It is possible that during the first 6 months of therapy, patients aged < 65 years were more willing to tolerate or had fewer severe AEs compared with the older adult group. It’s also possible that the smaller number of patients in the older adult group was due to increased AEs that led them to discontinue early (before completion of 6 months of therapy) and/or prescriber discomfort in using AOMs in the older adult population. In addition, because the specific medication(s) taken by patients in each group were not detailed, it is unknown whether the adult group was taking AOMs associated with a greater number of AEs.

Limitations

This was a retrospective study with a relatively small sample size. A larger sample size may have shown more precise differences between age groups and may be more representative of the general population. Additionally, data were reliant on appropriate documentation, and adherence to AOM therapy was not assessed due to the retrospective nature of this study. At times, the study relied on patient reported data points, such as weight, if a clinic weight was not available. Also, this study did not account for many potential confounding factors such as other medications taken by the patient, which can affect outcomes including weight, HbA_1c, LDL, blood pressure, and AEs.

Conclusions

This retrospective study of patients taking AOMs showed no statistically significant difference in weight loss at 6 or 12 months between adults aged < 65 years and older adults aged ≥ 65 years. A subset analysis found no statistically significant difference in change in body weight between specific age groups (18-40 years, 41-64 years, and ≥ 65 years). There was also no statistically significant difference in secondary outcomes, including change in HbA_1c (in patients with T2DM or prediabetes), LDL or BP between age groups. The safety endpoints showed a higher incidence of medication AEs in the adult group, with more of these adults discontinuing therapy due to AEs. This study indicates that AOM may have similar outcomes for weight loss and metabolic laboratory values/vital sign changes between adults and older adults. Also, our findings suggest that patients aged < 65 years may experience more AEs than patients aged ≥ 65 years after ≥ 6 months of AOM therapy. Larger studies are needed to further evaluate these age-specific findings.